Risk Factors for Falls Among Seniors: Implications of Gender
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite extensive literature on falls among seniors, little is known about gender-specific risk factors. To determine the prevalence of falls by gender and sociodemographic, lifestyle/behavioral, and medical factors, we conducted a cross-sectional study in a nationally representative sample of Canadian adults who were 65 years of age or older (n = 14,881) from the Canadian Community Health Survey-Healthy Aging (2008-2009). Logistic regression models were applied to investigate gender-specific associations between potential risk factors and falls. In men, stroke (odds ratio (OR) = 1.91), nutritional risk (OR = 1.86), post-secondary school degree (OR = 1.68), eye disorder (OR = 1.35), widowed/separated/divorced marital status (OR = 1.28), and arthritis (OR = 1.27) were independently associated with significantly higher odds of falls. In women, significant independent correlates of falls included stroke (OR = 1.53), age of 85 years or older (OR = 1.51), nutritional risk (OR = 1.39), consumption of at least 1 alcoholic drink per week (OR = 1.39), use of 5 or more medications (OR = 1.36), arthritis (OR = 1.36), diabetes (OR = 1.31), and osteoporosis (OR = 1.22). Higher physical activity levels were protective in both genders, and higher household income was protective in women. Gender should be considered when planning fall prevention strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it